The rapid emergence of sophisticated, dynamic, and rare or previously unseen attack pattern exposes fundamental limitations of conventional intrusion detection systems (IDS) based on static learning architectures. While deep learning (DL) models have demonstrated strong performance by capturing complex spatial and temporal traffic patterns, existing DL-based IDS largely rely on fixed decision structures, restricting adaptability to evolving threats. Furthermore, current hybrid DL-metaheuristic approaches typically use such metaheuristics as offline or auxiliary optimizers, without interacting with the deep model’s internal latent representations. This paper introduces a novel co-evolutionary IDS that establishes a tight, bidirectional coupling between DL and Particle Swarm Optimization (PSO) through latent-space-guided structural adaptation. A CNN-LSTM (Convolutional Neural Networks-Long Short-Term Memory) encoder learns discriminative spatial–temporal representations of network traffic, which dynamically guide PSO to select and optimize Adaptive Decision Blocks during training. Unlike prior hybrid methods, the proposed framework enables continuous co-evolution of both representation learning and decision structure, allowing the IDS to adapt its internal architecture in response to uncertain, rare, and previously unseen attack patterns. Comprehensive evaluations on UNSW-NB15, CICIDS2017, and ToN-IoT demonstrate statistically significant improvements over state-of-the-art DL and hybrid IDS approaches, achieving over 99.97% accuracy, recall and F1-score, and low-latency inference suitable for near real-time deployment.
Mjahed et al. (Thu,) studied this question.